Feng Liu (Assistant Professor at The University of Melbourne)
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Feng Liu, Ph.D.
Assistant Professor in Statistics (Data Science),
Visiting Scientist @ Imperfect Information Learning Team,
Visting Fellow @ DeSI Lab,
Address: Room 108, Old Geology Building (South Wing), |
Opportunities
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I am always looking for self-motivated PhD, MPhil, Research Assistants, and Visiting Researchers. Please see this page for recruiting information, and check this page for the school information. Meanwhile, I am happy to host remote research trainees. You can collaborate with many excellent researchers in the frontier machine learning research areas in our group. Check this page for more information.
Biography
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I am a machine learning researcher with research interests in hypothesis testing and trustworthy machine learning.
I am currently an Assistant Professor in Statistics (Data Science) at the School of Mathematics and Statistics, The University of Melbourne, Australia. We are also running the Trustworthy Machine Learning and Reasoning (TMLR) Lab where I am one of co-directors (see this page for details).
In addition, I am a Visiting Scientist at RIKEN-AIP, Japan, and a Visting Fellow at DeSI Lab, Australian Artificial Intelligence Institute, University of Technology Sydney. I was the recipient of the Australian Laureate postdoctoral fellowship. I received my Ph.D. degree in computer science at the University of Technology Sydney in 2020, advised by Dist. Prof. Jie Lu and Prof. Guangquan Zhang.
I was a research intern at the RIKEN-AIP, working on the robust domain adaptation project with Prof. Masashi Sugiyama, Dr. Gang Niu and Dr. Bo Han. I visited Gatsby Computational Neuroscience Unit at UCL and worked on the hypothesis testing project with Prof. Arthur Gretton, Dr. Danica J. Sutherland and Dr. Wenkai Xu. I have served as a senior program committee (SPC) member for ECAI and program committee (PC) members for NeurIPS, ICML, ICLR, AISTATS, ACML, AAAI, IJCAI, CIKM, ECAI and so on. I also serve as reviewers for many academic journals, such as IEEE-TPAMI, IEEE-TNNLS, IEEE-TFS, PR, AMM and so on. I received the Outstanding Reviewer Award of NeurIPS (2021), the Outstanding Reviewer Award of ICLR (2021), the UTS-FEIT HDR Research Excellence Award (2019), the Best Student Paper Award of FUZZ-IEEE (2019) and the UTS Research Publication Award (2018).
Research Interests
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My research interests lie in statistical hypothesis testing and trustworthy machine learning. Specifically, my current research work center around the following topics:
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Statistical Hypothesis Testing:
Two-sample Testing: Testing if two datasets are drawn from the same distribution.
Goodness-of-fit Testing: Testing if data are drawn from a given distribution.
Independence Testing: Testing if two datasets are independent.
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Trustworthy Machine Learning:
Defending against Adversarial Attacks: Detecting adversarial attacks (i.e., adversarial attack detection); Training a robust model against future adversarial attacks (i.e., adversarial training).
Being Aware of Out-of-distribution Data: Detecting out-of-distribution data; Training a robust model in the open world (e.g., open-set learning, out-of-distribution generalization).
Learning/Inference under Distribution Shift (a.k.a., Transfer Learning): Leveraging the knowledge from domains with abundant labels (i.e., source domains)/pre-trained models (i.e., source models) to complete classification/clustering tasks in an unlabeled domain (i.e., target domain), where two domains are different but related.
Protecting Data Privacy: Training a model to ensure that the training data will not be obtained by inverting the model (i.e., defending against model-inversion attacks).
Research Experience
Assistant Professor (Research Only) (May 2021--May 2022)
Australian Artificial Intelligence Institute, UTSAustralian Laureate Posdoc Researcher (May 2020--May 2021)
Australian Artificial Intelligence Institute, UTSVisiting Researcher (August 2019--November 2019)
Gatsby Computational Neuroscience Unit, UCL, London, UKResearch Intern (March 2019--July 2019)
Imperfect Information Learning Team, RIKEN-AIP, Tokyo, JapanResearch Assistant (July 2015--July 2016)
Institute of Statistical Science, School of Statistics,Research Intern (May 2014--September 2014)
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and
Advisor: Dist. Prof. Jie Lu
Project: Autonomous Transfer Learning
Advisor: Dist. Prof. Jie Lu
Project: Autonomous Transfer Learning
Advisor: Prof. Arthur Gretton
Collaborators: Dr. Danica J. Sutherland, Dr. Wenkai Xu
Project: Learning Deep Kernels for Two Sample Test
Advisor: Prof. Masashi Sugiyama
Collaborators: Dr. Gang Niu and Dr. Bo Han
Project: Wildly Unsupervised Domain Adaptation
Dongbei University of Finance and Economics, Dalian, China
Advisor: Prof. Ping Jiang
Collaborators: Prof. Jianzhou Wang, Yiliao Song
Project: Time Series Prediction and Interpolation
Geophysical Fluid Dynamics (LASG),
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Advisor: Senior Scientist Zhenhai Guo
Collaborators: Dr. Xia Xiao, Dr. Jing Zhao, Dr. Zhongyue Su
Project: Forecasting Long-Term Wind Speed via NWP ensembles
Education
Ph.D. in Computer Science (November 2020)
Faculty of Engineering and Information Technology,Master of Science (June 2015)
School of Mathematic and Statistics, Lanzhou University, Lanzhou, ChinaBachelor of Science (June 2013)
School of Mathematic and Statistics, Lanzhou University, Lanzhou, China
University of Technology Sydney, Sydney, Australia.
Supervised by Dist. Prof. Jie Lu and Prof. Guangquan Zhang
Supervised by Prof. Jianzhou Wang
